Instructions to use universalml/sd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/sd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/sd") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("universalml/sd") model = AutoModelForImageClassification.from_pretrained("universalml/sd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1e19318f8717ebc72937539d36a07f9e541c740391612990f31644a640ae78f
- Size of remote file:
- 343 MB
- SHA256:
- e788a18222ee26263e8b4d33f25647a1f8750adb0dae27e56a0a40b19820ea87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.